[TOC]
- Title: Multi-Level Compositional Reasoning for Interactive Instruction Following
- Author: Suvaansh Bhambri et. al.
- Publish Year: 2023
- Review Date: Fri, Mar 3, 2023
- url: https://ppolon.github.io/paper/aaai2023-alfred-mocha.pdf
Summary of paper
Motivation
- The task given to the agents are often composite thus are challenging as completing them require to reason about multiple subtasks.
Contribution
- we propose to divide and conquer it by breaking the task into multiple subgoals and attend to them individually for better navigation and interaction.
- at the highest level, we infer a sequence of human-interpreatable subgoals to be executed based on the language instructions by a high-level policy composition controller.
- at the middle level, we discriminatively control the agent’s navigation by a master policy by alternating between a navigation policy and various independent interaction policies.
- finally, at the lowest level, we infer manipulation actions with the corresponding object masks using appropriate interaction policy.
Model